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arXiv · 2211.14144

Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size Data

Can Chen·Scott T. Weiss·Yang-Yu Liu
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Abstract

Feature selection is a powerful dimension reduction technique which selects a subset of relevant features for model construction. Numerous feature selection methods have been proposed, but most of them fail under the high-dimensional and low-sample size (HDLSS) setting due to the challenge of overfitting. In this paper, we present a deep learning-based method - GRAph Convolutional nEtwork feature Selector (GRACES) - to select important features for HDLSS data. We demonstrate empirical evidence that GRACES outperforms other feature selection methods on both synthetic and real-world datasets.

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Can Chen, Scott T. Weiss, Yang-Yu Liu. 2022-11-25. Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size Data. https://doi.org/10.1093/bioinformatics%2Fbtad135

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